535 lines
21 KiB
Python
535 lines
21 KiB
Python
# coding=utf-8
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only Nemotron model compatible with HuggingFace weights."""
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from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig, LoRAConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader, maybe_remap_kv_scale_name)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors, SamplerOutput
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from vllm.transformers_utils.configs import NemotronConfig
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from .interfaces import SupportsLoRA
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from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
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# The architecture is pretty similar to Llama, with these changes:
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# - There is no gate_proj, just up_proj
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# - Normal LayerNorm (with a +1 to the weights) instead of RMSNorm
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# - Squared ReLU instead of SwiGLU
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# - Adds a partial_rotary_factor to RoPE
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def _cast_if_autocast_enabled(*args):
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if not torch.is_autocast_enabled():
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return args
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else:
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return torch.cuda.amp.autocast_mode._cast(
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args, torch.get_autocast_gpu_dtype())
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class NemotronLayerNorm1P(nn.LayerNorm):
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def __init__(self,
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normalized_shape: Union[int, List[int], torch.Size],
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eps: float = 1e-5,
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elementwise_affine: bool = True,
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bias: bool = True,
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device=None,
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dtype=None):
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super().__init__(normalized_shape, eps, elementwise_affine, bias,
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device, dtype)
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def forward(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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if residual is not None:
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x = x + residual
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residual = x
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args = _cast_if_autocast_enabled(x, self.normalized_shape,
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self.weight + 1, self.bias, self.eps)
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with torch.cuda.amp.autocast(enabled=False):
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x = torch.nn.functional.layer_norm(*args)
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return x if residual is None else (x, residual)
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class NemotronMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.up_proj = ColumnParallelLinear(input_size=hidden_size,
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output_size=intermediate_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.up_proj")
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self.down_proj = RowParallelLinear(input_size=intermediate_size,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.down_proj")
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self.act_fn = get_act_fn(hidden_act)
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def forward(self, x):
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up, _ = self.up_proj(x)
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x = self.act_fn(up)
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x, _ = self.down_proj(x)
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return x
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class NemotronAttention(nn.Module):
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def __init__(
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self,
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config: NemotronConfig,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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cache_config: Optional[CacheConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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# MistralConfig has an optional head_dim introduced by Mistral-Nemo
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self.head_dim = getattr(config, "head_dim",
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self.hidden_size // self.total_num_heads)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.partial_rotary_factor = config.partial_rotary_factor
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self.max_position_embeddings = max_position_embeddings
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self.qkv_proj = QKVParallelLinear(
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hidden_size=hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=self.total_num_kv_heads,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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self.o_proj = RowParallelLinear(
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input_size=self.total_num_heads * self.head_dim,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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partial_rotary_factor=self.partial_rotary_factor,
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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output, _ = self.o_proj(attn_output)
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return output
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class NemotronDecoderLayer(nn.Module):
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def __init__(
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self,
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config: NemotronConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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if rope_scaling is not None and getattr(
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config, "original_max_position_embeddings", None):
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rope_scaling["original_max_position_embeddings"] = (
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config.original_max_position_embeddings)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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# Support abacusai/Smaug-72B-v0.1 with attention_bias
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# Support internlm/internlm-7b with bias
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attention_bias = getattr(config, "attention_bias", False) or getattr(
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config, "bias", False)
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self.self_attn = NemotronAttention(
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config=config,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=getattr(config, "num_key_value_heads",
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config.num_attention_heads),
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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bias=attention_bias,
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cache_config=cache_config,
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prefix=f"{prefix}.self_attn",
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)
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self.mlp = NemotronMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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bias=getattr(config, "mlp_bias", False),
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = NemotronLayerNorm1P(config.hidden_size,
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eps=config.norm_eps)
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self.post_attention_layernorm = NemotronLayerNorm1P(
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config.hidden_size, eps=config.norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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class NemotronModel(nn.Module):
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def __init__(
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self,
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config: NemotronConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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lora_config: Optional[LoRAConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.padding_idx = config.pad_token_id
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lora_vocab = (lora_config.lora_extra_vocab_size *
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(lora_config.max_loras or 1)) if lora_config else 0
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self.vocab_size = config.vocab_size + lora_vocab
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self.org_vocab_size = config.vocab_size
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if get_pp_group().is_first_rank or (config.tie_word_embeddings
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and get_pp_group().is_last_rank):
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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)
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else:
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self.embed_tokens = PPMissingLayer()
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: NemotronDecoderLayer(config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix),
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prefix=f"{prefix}.layers")
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if get_pp_group().is_last_rank:
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self.norm = NemotronLayerNorm1P(config.hidden_size,
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eps=config.norm_eps)
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else:
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self.norm = PPMissingLayer()
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors],
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions,
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hidden_states,
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kv_caches[i - self.start_layer],
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attn_metadata,
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residual,
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({
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"hidden_states": hidden_states,
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"residual": residual
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})
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class NemotronForCausalLM(nn.Module, SupportsLoRA):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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}
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# LoRA specific attributes
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supported_lora_modules = [
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"qkv_proj", "o_proj", "up_proj", "down_proj", "embed_tokens", "lm_head"
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]
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embedding_modules = {
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"embed_tokens": "input_embeddings",
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"lm_head": "output_embeddings",
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}
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embedding_padding_modules = ["lm_head"]
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bitsandbytes_stacked_params_mapping = {
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# shard_name, weight_name, index
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"q_proj": ("qkv_proj", 0),
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"k_proj": ("qkv_proj", 1),
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"v_proj": ("qkv_proj", 2),
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}
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def __init__(
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self,
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config: NemotronConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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lora_config: Optional[LoRAConfig] = None,
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) -> None:
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super().__init__()
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assert isinstance(config, NemotronConfig)
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self.config = config
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self.lora_config = lora_config
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self.model = NemotronModel(config,
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cache_config,
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quant_config,
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lora_config=lora_config,
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prefix="model")
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if get_pp_group().is_last_rank:
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self.unpadded_vocab_size = config.vocab_size
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if lora_config:
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self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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padding_size=DEFAULT_VOCAB_PADDING_SIZE
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# We need bigger padding if using lora for kernel
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# compatibility
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if not lora_config else lora_config.lora_vocab_padding_size,
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quant_config=quant_config,
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)
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if config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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logit_scale = getattr(config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size,
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logit_scale)
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self.sampler = Sampler()
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else:
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self.lm_head = PPMissingLayer()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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model_output = self.model(input_ids, positions, kv_caches,
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attn_metadata, intermediate_tensors)
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return model_output
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def make_empty_intermediate_tensors(
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self, batch_size: int, dtype: torch.dtype,
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device: torch.device) -> IntermediateTensors:
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return IntermediateTensors({
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"hidden_states":
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torch.zeros((batch_size, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device),
|
|
"residual":
|
|
torch.zeros((batch_size, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device),
|
|
})
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
(".qkv_proj", ".q_proj", "q"),
|
|
(".qkv_proj", ".k_proj", "k"),
|
|
(".qkv_proj", ".v_proj", "v"),
|
|
]
|
|
params_dict = dict(self.named_parameters())
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if ("rotary_emb.cos_cached" in name
|
|
or "rotary_emb.sin_cached" in name):
|
|
# Models trained using ColossalAI may include these tensors in
|
|
# the checkpoint. Skip them.
|
|
continue
|
|
for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|